Abstract
Background subtraction is commonly used to detect foreground objects in video surveillance. Traditional background subtraction methods are usually based on the assumption that the background is stationary. However, they are not applicable to dynamic background, whose background images change over time. In this paper, we propose an adaptive Local-Patch Gaussian Mixture Model (LPGMM) as the dynamic background model for detecting moving objects from video with dynamic background. Then, the SVM classification is employed to discriminate between foreground objects and shadow regions. Finally, we show some experimental results on several video sequences to demonstrate the effectiveness and robustness of the proposed method. © 2011 IEEE.